---
title: Set up accuracy monitoring
description: Configure accuracy monitoring on a deployment's Accuracy Settings tab.

---

# Set up accuracy monitoring {: #set-up-accuracy-monitoring }

You can monitor a deployment for accuracy using the [**Accuracy**](deploy-accuracy) tab, which lets you analyze the performance of the model deployment over time using standard statistical measures and exportable visualizations. You can enable accuracy monitoring on the **Accuracy > Settings** tab. To configure accuracy monitoring, you must:

* [Enable target monitoring](#enable-target-monitoring) in the [Data Drift Settings](data-drift-settings)

* [Select an association ID](#select-an-association-id) in the Accuracy Settings

* [Add actuals](#add-actuals) in the Accuracy Settings

On a deployment's **Accuracy Settings** page, you can configure the **Association ID** and **Upload Actuals** settings and the accuracy monitoring **Definition** and **Notifications** settings:

![](images/accuracy-settings.png)

|             Field             |  Description  |
|-------------------------------|---------------|
|  **Association ID**           | :~~: |
| [Association ID](#select-an-association-id) | Defines the name of the column that contains the association ID in the prediction dataset for your model. Association IDs are required for setting up accuracy monitoring in a deployment. The association ID functions as an identifier for your prediction dataset so you can later match up outcome data (also called "actuals") with those predictions. |
| [Require association ID in prediction requests](#select-an-association-id) | Requires your prediction dataset to have a column name that matches the name you entered in the Association ID field. When enabled, you will get an error if the column is missing. |
| [Enable automatic actuals feedback for time series models](#association-ids-for-time-series-deployments) | For time series deployments that have indicated an association ID, this setting enables the automatic submission of actuals so that you do not need to submit them manually via the UI or API. Once enabled, actuals can be extracted from the data used to generate predictions. As each prediction request is sent, DataRobot can extract an actual value for a given date. This is because when you send prediction rows to forecast, historical data is included. This historical data serves as the actual values for the previous prediction request. |
|  **Upload Actuals**           | :~~: |
| [Drop file(s) here or choose file](#add-actuals) |  Uploads a file with actuals to monitor accuracy by matching the model's predictions with actual values. Actuals are required to enable the [**Accuracy**](deploy-accuracy) tab. |
| [Assigned features](#assigned-features) | Configures the **Assigned features** settings after you upload actuals. |
|  **Definition**           | :~~: |
| [Set definition](#define-accuracy-monitoring-notifications) | Configures the metric, measurement, and threshold definitions for accuracy monitoring. |
|  **Notifications**           | :~~: |
| [Send notification](#schedule-accuracy-monitoring-notifications) | Configures the accuracy monitoring notification schedule. |

## Enable target monitoring {: #enable-target-monitoring}

In order to enable accuracy monitoring, you must also [enable target monitoring](data-drift-settings) in the **Data Drift** section of the **Data Drift Settings** tab.

![](images/target-monitoring-for-accuracy.png)

If target monitoring is turned off, a message displays on the **Accuracy** tab to remind you to enable target monitoring.

##  Select an association ID {: #select-an-association-id }

To activate the [**Accuracy** tab](deploy-accuracy) for a deployment, first designate an association ID in the prediction dataset. The association ID is a [foreign key](https://www.tutorialspoint.com/Foreign-Key-in-RDBMS){ target=_blank }, linking predictions with future results (or [actuals](glossary/index#actuals)). It corresponds to an event for which you want to track the outcome; For example, you may want to track a series of loans to see if any of them have defaulted or not.

!!! important
    You must set an association ID _before_ making predictions to include those predictions in accuracy tracking.

On the **Accuracy > Settings** tab of a deployment, the **Association ID** section has a field for the column name containing the association IDs. The column name you define in the **Association ID** field must match the name of the column containing the association IDs in the prediction dataset for your model. Each cell for this column in your prediction dataset should contain a unique ID that pairs with a corresponding unique ID that occurs in the actuals payload. 

![](images/association-id-definition.png)

In addition, you can enable **Require association ID in prediction requests** to throw an error if the column is missing from your prediction dataset when you make a prediction request.

You can set the column name containing the association IDs on a deployment at any time, whether predictions have been made against that deployment or not. Once set, you can only update the association ID if you have not yet made predictions that include that ID. Once predictions have been made using that ID, you cannot change it.

Association IDs (the contents in each row for the designated column name) must be shorter than 128 characters, or they will be truncated to that size. If truncated, uploaded actuals will require the truncated association IDs for your actuals in order to properly generate accuracy statistics.

??? faq "How does an association ID work?"
    For an example of an association ID, look at this sample dataset of transactions:

    ![](images/dnt-accuracy-13.png)

    The third column, `transaction_num`, is the column containing the association IDs. A row's unique ID (`transaction_num` in this example) groups together the other features in that row (`transaction_amnt` and `annual_inc` in this example), creating an "association" between the related feature values. Defining `transaction_num` as the column containing association IDs allows DataRobot to use these unique IDs to associate each row of prediction data and predicted outcome with the actual outcome later. Therefore, `transaction_num` is what you would enter in the **Association ID** field when setting up accuracy.

###  Association IDs for time series deployments {: #association-ids-for-time-series-deployments }

For time series deployments, prediction requests already contain the data needed to uniquely identify individual predictions. Therefore, it is important to consider the feature used as an association ID, depending on the deployment type, consider the following guidelines:

* **Single-series deployments**: DataRobot recommends using the `Forecast Date` column as the association ID because it is the date you are making predictions for. For example, if today is June 15th, 2022, and you are forecasting daily total sales for a store, you may wish to know what the sales will be on July 15th, 2022. You will have a single actual total sales figure for this date, so you can use “2022-07-15” as the association ID (the forecast date).

* **Multiseries deployments**: DataRobot recommends using a custom column containing `Forecast Date + Series ID` as the association ID. If a single model can predict daily total sales for a number of stores, then you can use, for example, the association ID “2022-07-15 1234” for sales on July 15th, 2022 for store #1234.

* **All time series deployments**: You may want to forecast the same date multiple times as the date approaches. For example, you might forecast daily sales 30 days in advance, then again 14 days in advance, and again 7 days in advance. These forecasts all have the same forecast date, and therefore the same association ID.

    ![](images/forecast-overview.png)

!!! important
    Be aware that models may produce different forecasts when predicting closer to the forecast date. Predictions for multiple forecast distances are each tracked individually so that accuracy can be properly calculated for each forecast distance.

After you designate an association ID, you can toggle **Enable automatic actuals feedback for time series models** to on. This setting automatically sumbmits actuals so that you do not need to submit them manually via the UI or API. Once enabled, actuals can be extracted from the data used to generate predictions. As each prediction request is sent, DataRobot can extract an actual value for a given date. This is because when you send prediction rows to forecast, historical data is included. This historical data serves as the actual values for the previous prediction request.

## Add actuals {: #add-actuals }

You can directly upload datasets containing actuals to a deployment from the **Accuracy > Settings** tab (described here) or through the [API](#upload-actuals-with-the-api). The deployment's prediction data must correspond to the actuals data you upload. Review the [row limits](#actuals-upload-limit) for uploading actuals before proceeding.

1. To use actuals with your deployment, in the **Upload Actuals** section, click **Choose file**. Either upload a file directly or select a file from the [**AI Catalog**](catalog). If you upload a local file, it is added to the **AI Catalog** after successful upload. Actuals must be snapshotted in the AI Catalog to use them with a deployment.

2. Once uploaded, complete the fields that populate in the **Actuals** section. Under <span id="assigned-features">**Assigned features**</span>, each field has a dropdown menu that allows you to select any of the columns from your dataset:

    ![](images/accuracy-actuals-assigned-features.png)

	|          Field          | Description |
	|-------------------------|-------------|
	| Actual Response         | Defines the column in your dataset that contains the actual values. |
	| Association ID          | Defines the column that contains the [association IDs](#select-an-association-id). |
	| Timestamp (optional)    | Defines the column that contains the date/time when the actual values were obtained, formatted according to [RFC 3339](https://tools.ietf.org/html/rfc3339){ target=_blank } (for example, 2018-04-12T23:20:50.52Z).  |

    ??? note "Column name matching"
        The column names for the association ID in the prediction and the actuals datasets do not need to match. The only requirement is that each dataset contains a column that includes an identifier that does match the other dataset. For example, if the column `store_id` contains the association ID in the prediction dataset that you will use to identify a row and match it to the actual result, enter `store_id` in the **Association ID** section. In the **Upload Actuals** section under **Assigned fields**, in the **Association ID** field, enter the name of the column in the actuals dataset that contains the identifiers associated with the identifiers in the prediction dataset.

        ![](images/assoc-id-column-name.png)

3. After you configure the **Assigned fields**, click **Save**. 
    
    When you complete this configuration process and [making predictions](../../predictions/index) with a dataset containing the defined **Association ID**, the [**Accuracy**](deploy-accuracy) page is enabled for your deployment.

##  Upload actuals with the API {: #upload-actuals-with-the-api }

This workflow outlines how to enable the **Accuracy** tab for deployments using the DataRobot API.

1. From the **Accuracy > Settings** tab, locate the **Association ID** section.

2. In the **Association ID** field, enter the column name containing the association IDs in your prediction dataset.

3. Enable **Require association ID in prediction requests**. This requires your prediction dataset to have a column name that matches the name you entered in the **Association ID** field. You will get an error if the column is missing.

	!!! note
	     You can set an association ID and *not* toggle on this setting if you are sending prediction requests that do not include the association ID and you do not want them to error; however, until it is enabled, you cannot monitor accuracy for your deployment.

4. [Make predictions](../../predictions/index) using a dataset that includes the association ID.

5. Submit the actual values via the DataRobot API (for details, refer to the API documentation by signing in to DataRobot, clicking the question mark on the upper right, and selecting **API Documentation**; in the API documentation, select **Deployments > Submit Actuals - JSON**). You should review the [row limits](#actuals-upload-limit) for uploading actuals before proceeding.

    !!! note
        The actuals payload must contain the `associationId` and `actualValue`, with the column names for those values in the dataset defined during the upload process. If you submit multiple actuals with the same association ID value, either in the same request or a subsequent request, DataRobot updates the actuals value; however, this update doesn't recalculate the metrics previously calculated using that initial actuals value. To recalculate metrics, you can [clear the deployment statistics](actions-menu#clear-deployment-statistics) and reupload the actuals (or create a new deployment).

	Use the following snippet in the API to submit the actual values:

    ``` python
    import requests


    API_TOKEN = ''
    USERNAME = 'johndoe@datarobot.com'
    DEPLOYMENT_ID = '5cb314xxxxxxxxxxxa755'
    LOCATION = 'https://app.datarobot.com'


    def submit_actuals(data, deployment_id):
        headers = {'Content-Type': 'application/json', 'Authorization': 'Token {}'.format(API_TOKEN)}
        url = '{location}/api/v2/deployments/{deployment_id}/actuals/fromJSON/'.format(
            deployment_id=deployment_id, location=LOCATION
        )
        resp = requests.post(url, json=data, headers=headers)
        if resp.status_code >= 400:
            raise RuntimeError(resp.content)
        return resp.content


    def main():
        deployment_id = DEPLOYMENT_ID
        payload = {
            'data': [
                {
                    'actualValue': 1,
                    'associationId': '5d8138fb9600000000000000',  # str
                },
                {
                    'actualValue': 0,
                    'associationId': '5d8138fb9600000000000001',
                },
            ]
        }
        submit_actuals(payload, deployment_id)
        print('Done')


    if __name__ == "__main__":
        main()
    ```

    After submitting at least 100 actuals for a non-time series deployment (there is no minimum for time series deployments) and making predictions with corresponding association IDs, the [**Accuracy**](deploy-accuracy) tab becomes available for your deployment.

??? note "Actuals upload limit"
    The <span id="actuals-upload-limit">number of actuals you can upload to a deployment is limited</span> _per request_ and _per hour_. These limits vary depending on the endpoint used:

    Endpoint      | Upload limit
    --------------|-------------
    `fromJSON`    | <ul><li>10,000 rows per request</li><li>10,000,000 rows per hour</li></ul>
    `fromDataset` | <ul><li>5,000,000 rows per request</li><li>10,000,000 rows per hour</li></ul>

## Define accuracy monitoring notifications {: #define-accuracy-monitoring-notifications }

For accuracy, the notification conditions relate to a [performance optimization metric](opt-metric) for the underlying model in the deployment. Select from the same set of metrics that are available on the Leaderboard. You can visualize accuracy using the [Accuracy over Time graph](deploy-accuracy#accuracy-over-time-graph) and the [Predicted & Actual graph](deploy-accuracy#predicted-actual-graph). Accuracy monitoring is defined by a single accuracy rule. Every 30 seconds, the rule evaluates the deployment's accuracy. Notifications trigger when this rule is violated. 

Before configuring accuracy notifications and monitoring for a deployment, set an [association ID](accuracy-settings#association-id). If not set, DataRobot displays the following message when you try to modify accuracy notification settings:

![](images/notify-4.png) 

!!! note
    Only deployment _Owners_ can modify accuracy monitoring settings. They can set no more than one accuracy rule per deployment. _Consumers_ cannot modify monitoring or notification settings. _Users_ can [configure the conditions under which notifications are sent to them](deploy-notifications) and see explained status information by hovering over the accuracy status icon:

    ![](images/notify-8.png)

To set up accuracy monitoring:
  
1. On the **Accuracy Settings** page, in the **Definition** section, configure the settings for monitoring accuracy:

    ![](images/accuracy-monitoring-definition.png)

    |   | Element | Description |
    |---|---------|-------------|
    | ![](images/icon-1.png) | Metric | Defines the metric used to evaluate the accuracy of your deployment. The metrics available from the dropdown menu are the same as those [supported by the **Accuracy** tab.](deploy-accuracy#available-accuracy-metrics)|
    | ![](images/icon-2.png) | Measurement | Defines the unit of measurement for the accuracy metric and its thresholds. You can select **value** or **percent** from the dropdown. The **value** option measures the metric and thresholds by specific values, and the **percent** option measures by percent changed. The **percent** option is unavailable for model deployments that don't have training data. |
    | ![](images/icon-3.png) | "At Risk" / "Failing" thresholds  | Sets the values or percentages that, when exceeded, trigger notifications. Two thresholds are supported: when the deployment's accuracy is "At Risk" and when it is "Failing." DataRobot provides default values for the thresholds of the first accuracy metric provided (LogLoss for classification and RMSE for regression deployments) based on the deployment's training data. Deployments without training data populate default threshold values based on their prediction data instead. If you change metrics, default values are not provided. |

    !!! note
        Changes to thresholds affect the periods of time in which predictions are made across the entire history of a deployment. These updated thresholds are reflected in the performance monitoring visualizations on the [Accuracy](deploy-accuracy) tab. 

2. After updating the accuracy monitoring settings, click **Save**.


### Examples of accuracy monitoring settings

Each combination of metric and measurement determines the expression of the rule. For example, if you use the LogLoss metric measured by value, the rule triggers notifications when accuracy "is greater than" the values of 5 or 10:

![](images/notify-6.png)

However, if you change the metric to AUC and the measurement to percent, the rule triggers notifications when accuracy "decreases by" the values set for the threshold:

![](images/notify-7.png)

## Schedule accuracy monitoring notifications {: #schedule-accuracy-monitoring-notifications }

To schedule accuracy monitoring email notifications:

1. On the **Accuracy Settings** page, in the **Notifications** section, select the **Send notifications** checkbox. 

2. Configure the settings for accuracy notifications.

    The following table lists the scheduling options. All times are displayed in your configured time zone:

    | Frequency       | Description |
    |-----------------|-------------|
    | Every day       | Each day at the configured hour* |
    | Every week      | Configurable day and hour        |
    | Every month     | Configurable date and hour       |
    | Every quarter   | Configurable number of days (`1`-`31`) past the first day of January, April, July, October, at the configured hour |

    _* The cadence setting applies across all days selected. In other words, you cannot set checks to occur every 12 hours on Saturday and every 2 hours on Monday._

3. After updating the scheduling settings, click **Save**. 
    
    At the configured time, DataRobot sends emails to subscribers.